Presentation on theme: "Behavioral Finance Alok Kumar Yale School of Management 8 December 1999."— Presentation transcript:
Behavioral Finance Alok Kumar Yale School of Management 8 December 1999
Agenda Efficient Market Hypothesis (EMH) Expected Utility; Rational Expectations Few Examples Prospect Theory (Kahneman and Tversky) Behavioral Heuristics and Biases in Decision Making Implications for Financial Markets
Market Efficiency Fama: “The market price at any time instant reflects all available information in the market”. Cannot “make money” using “stale information”. Three forms –Weak form: past prices and returns. –Semi-strong form: all public information. –Strong form: all public AND private information. Michael Jensen: “there is no other proposition in economics which has more empirical support than the EMH”.
Challenges to EMH Investors are not “fully rational”. They exhibit “biases” and use simple “heuristics” (rules of thumb) in making decisions. Empirical Evidence on investor behavior: –investors fail to diversify. –investors trade actively (Odean). –Investors may sell winning stocks and hold onto losing stocks (Odean). –extrapolative and contrarian forecasts.
Expected Utility Theory A theory of choice under uncertainty for a single decision-maker. Expected Utility = p1*u1 + p2*u2 + … + pn*un. p: probability of an event u: utility derived from the event Based on several strong assumptions about preferences. Example: transitivity, cancellation.
Rational Expectations Paradigm All investors are identical. All investors are utility maximizers. All investors use “Bayes rule” to form new beliefs as new information becomes available. All investor predictions are accurate. Expected Utility + Rational Expectations => Market Efficiency
Are Financial Markets Efficient? Weak form of market efficiency supported to a certain extent. Challenges: –Excess market volatility –Stock price over-reaction: long time trends (1-3 years) reverse themselves. –Momentum in stock prices: short-term trends (6-12 months) continue. –Size and B/M ratio (stale information) may help predict returns.
Stock Price Reaction to Non-Information Crash of 1987: 22.6% decline without any apparent news. 50 largest one-day stock price movements: occurred on days of no major announcements. Inclusion of a stock in the S&P500 index results in significant share price reactions. Example: AOL rose 18% on the news of its inclusion in the index.
Role of Investor Behavior Bounded Rationality: “satisficing” behavior. Information processing limitations. Example: memory limitations. Investor Sentiment: beliefs based on heuristics rather than Bayesian rationality. Investors may react to “irrelevant information” and hence may trade on “noise” rather than information.
“Irrational” Behavior of Professional Money Managers May choose a portfolio very close to the benchmark against which they are evaluated (for example: S&P500 index). Herding: may select stocks that other managers select to avoid “falling behind” and “looking bad”. Window-dressing: add to the portfolio stocks that have done well in the recent past and sell stocks that have recently done poorly.
An Example Initial endowment: $300. Consider a choice between: –a sure gain of $100 –a 50% chance to gain $200, a 50% chance to gain $0. Initial endowment: $500. Consider a choice between: –a sure loss of $100 –a 50% chance to lose $200, a 50% chance to lose $0.
Reversal in Choice Case 1: 72% chose option 1, 28% chose option 2. Case 2: 36% chose option 1, 64% chose option 2. => A reversal in Choice Problem framed as a gain: decision maker is risk averse. Problem framed as a loss: decision maker is risk seeking.
Allais Paradox Case 1: consider a choice between: –$1 million with certainty. –$5 million with prob 0.1, $1m with prob 0.89 and $0 with prob 0.01 Case 2: consider a choice between: –$1m with prob 0.11, $0 with prob –$5m with prob 0.10 and $0 with prob 0.90.
Prospect Theory Proposed by two psychologists: Daniel Kahneman and Amos Tversky. Gambles are evaluated relative to a reference point. Decision maker analyzes “gains” and “losses” differently. Incremental value of a loss is larger than that of a loss. “the hurt of a $1000 loss is more painful than the benefit of a $1000 gain”.
Behavioral Heuristics and Decision-Making Biases What strategies do decision makers use when faced with difficult decisions, especially ones that involve uncertainty? Commonly Used Heuristics –Availability: “familiarity breeds investment”. –Representativeness: judgement based on similarity. “Patterns in random sequences”. –Reliance on the judgement of other people (Keynes beauty contest analogy).
Gambler’s Fallacy Investors may apply law of large numbers to small sequences. Example: fair coin tossing. THTHTHHHHHH -> P(T) = ?, P(H) = ?. Which of the 2 sequences is more likely to occur in a fair coin tossing experiment? –HHHHHHTTTTTTHHHHHH –HHTHTHHTHTTHTHHTTH
Some more Heuristics Overconfidence: people overestimate the reliability of their knowledge. –Excessive trading Framing Effect Regret Aversion: anticipation of a future regret can influence current decision. Disposition Effect: sell winners, hold on to the losers. Anchoring and adjustment: can create under-reaction.
Fashions and Fads People are influenced by each other. There is a social pressure to conform. Herding behavior: “safety-in-numbers”. Informational Cascades Positive Feedback Example: excessive demand for internet IPOs. Extremely high opening day returns.
Can arbitrage opportunities exist? Yes! –Real-world arbitrage is always risky. No riskless hedge for the arbitrageur. –Arbitrageur faces“noise trader” risk: mispricing can become worse before it disappears. –Close substitutes (needed for arbitrage positions) may not be available. –Fundamentally identical assets may NOT sell at identical prices.
Behavioral Finance: Two Major Foundations Investor Sentiment: creates disturbances to efficient prices. Limited arbitrage: arbitrage is never riskfree, hence it does not counter irrational disturbances. –Prices may not react to information by the “right” amount. –Prices may react to non-information. –Markets may remain efficient.
Summary Investor behavior does have an impact on the behavior of financial markets. How much? Not clear! Both “social” and “psychological” must be taken into account in explaining the behavior of financial markets. Market “anomalies” may be widespread. Behavioral Finance: does not replace but complements traditional models in Finance.